[Colloquium] Re: talk by Sarath Chandar this Friday at 11 am

[Karl Stratos via Colloquium]

Final reminder: the talk will happen in an hour and a half.

Best,
Karl

On Thu, Jun 29, 2017 at 4:01 PM, Karl Stratos <stratos at ttic.edu> wrote:

> Just a friendly reminder on the talk tomorrow at 11.
>
> Best,
> Karl
>
> On Tue, Jun 27, 2017 at 11:26 AM, Karl Stratos <stratos at ttic.edu> wrote:
>
>> Hi all,
>>
>> Sarath Chandar from the University of Montreal is visiting TTIC this
>> Friday. His talk is at 11 am and he will be around for the day until 5 pm.
>>
>> Please let me know if you'd like to meet with the speaker before/after
>> the talk, and if you'd like to join for lunch.
>>
>>
>>
>> When:     Friday, June 30th at 11:00 am
>>
>> Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room 526
>>
>> Who:      Sarath Chandar, University of Montreal
>>
>> *Title:* Memory Augmented Neural Networks
>>
>>
>> *Abstract:*
>>
>> Designing of general-purpose learning algorithms is a long-standing goal
>> of artificial intelligence. A general purpose AI agent should be able to
>> have a memory that it can store and retrieve information from. Despite the
>> success of deep learning in particular with the introduction of LSTMs and
>> GRUs to this area, there are still a set of complex tasks that can be
>> challenging for conventional neural networks. Those tasks often require a
>> neural network to be equipped with an explicit, external memory in which a
>> larger, potentially unbounded, set of facts need to be stored. They include
>> but are not limited to, reasoning, planning, episodic question-answering
>> and learning compact algorithms. Recently two promising approaches based on
>> neural networks to this type of tasks have been proposed: Memory Networks
>> and Neural Turing Machines.
>>
>>
>>
>> In this talk, I will give an overview of this new paradigm of "neural
>> networks with memory". I will present a unified architecture for Memory
>> Augmented Neural Networks (MANN) and discuss the ways in which one can
>> address the external memory and hence read/write from it. In the second
>> half of the talk, we will focus on recent advances in MANN which focus on
>> the following questions: How can we read/write from an extremely large
>> memory in a scalable way? How can we design efficient non-linear addressing
>> schemes using hard attention? How can we model long term dependencies in a
>> problem using MANNs? The answer to any one of these questions introduces a
>> variant of MANN. I will conclude the talk with several open challenges in
>> MANN.
>>
>>
>>
>> *Speaker Bio:* Sarath Chandar is currently a PhD student in University
>> of Montreal under the supervision of Yoshua Bengio and Hugo Larochelle. His
>> work mainly focuses on Deep Learning for complex NLP tasks like question
>> answering and dialog systems. He also investigates scalable training
>> procedure and memory access mechanisms for memory network architectures. In
>> the past, he has worked on multilingual representation learning and
>> transfer learning across multiple languages. His research interests include
>> Machine Learning, Natural Language Processing, Deep Learning, and
>> Reinforcement Learning. Before joining University of Montreal, he was a
>> Research Scholar in IBM Research India for a year. He has completed his MS
>> by Research in IIT Madras. To view the complete publication list and
>> speaker profile, please visit: http://sarathchandar.in/
>>
>> Host: Karl Stratos
>>
>
>
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